STATISTICAL METHODS FOR CORRELATING
MICROBIOME AND OTHER -OMICS DATA

Abstract

Understanding the relationship between microbiome and other omics data types is important both for obtaining a more comprehensive view of biological systems as well as for elucidating mechanisms underlying outcomes and response to exposures. However, the key features of microbiome data, including high-dimensionality, compositionality, sparsity, phylogenetic constraints, and complexity of relationships among taxa, pose a grand challenge for statistical analysis. This is compounded by the inherent complexity of the other omics data types as well. Recognizing these difficulties, we propose new methods for studying both community level correlations between microbiome and other data types as well as for correlating individual omic features with community composition. We particularly use a generalized measure of multivariate dependence called the kernel RV coefficient which can efficiently measure dependence between microbiome and other omics data while accommodating important structure in the data. Simulation studies show that our approach can often accurately identify true associations while correctly controlling the false positive rate. We illustrate our approach on a study examining the association between host genomics and microbiome composition in IBD patients.

Date:

Thursday, November 29, 2018

Time:

11:45 A.M. - 12:45 P.M.

Location:

Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York